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化学反应是如何计算的? 精选

已有 5346 次阅读 2021-8-4 10:54 |个人分类:新观察|系统分类:海外观察

化学反应是如何计算的?

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Figure 1. Schematic representation of native chemical computation.

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Figure 2. Correspondence between the automata hierarchy, the Chomsky hierarchy of languages on the left, and the experimental realizations of native chemical automata on the right side of the figure. All these hierarchies are inclusive, e.g., the BZ reaction can be shown (Dueñas-Díez and Pérez-Mercader, 2019a2020) to recognize not only a context sensitive language (L3) but also a context-free language (L2) and a regular language (L1). 

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Figure 3. Oscillatory reactions contain the necessary components for computation.

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Figure 4. Effects on the dominant pathways in the extended FKN model of the BZ reaction mechanism, as well as on the oscillatory metrics f and D due to the specific assignment of alphabet symbols in the implementation of a chemical Turing machine built using the BZ oscillatory chemistry, Here the aliquots for the symbols are (a) Sodium Bromate, (b) Malonic Acid, (c) NaOH, and (#) Catalyst Ru(II). The colored areas represent the set of reaction pathways affected by each of the alphabet symbols. In the case of (a,b,#) the highlighted reactions are enhanced, whereas in the case of (c) the highlighted reactions are slowed down. Reprinted from Dueñas-Díez and Pérez-Mercader (2019a). 

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Figure 5. [D, f ] plot at the end of computation. Panel (A) shows experimental results (each sequence experiment was repeated three times from which the plotted error bars were calculated) while panel (B) shows simulation results based on a modified FKN model (cf. Appendices 1 and 2). The locus of accepted words divides the plane in two disjoint regions of rejected words. The relative position of accepted and rejected sequences is reproduced qualitatively by the simulations. Panel (A) is Reprinted from Dueñas-Díez and Pérez-Mercader (2019a) Copyright (2019) with permission from Cell Press.

据美国圣菲研究所(Santa Fe Institute202184日提供的消息,该研究所的研究人员与美国哈佛大学(Harvard University)和西班牙雷普索尔技术实验室(Repsol Technology Lab, Madrid, Spain)的研究人员合作撰写了一篇综述性文章,对于化学反应是如何进行计算(How chemical reactions compute)的相关问题进行了阐述,相关研究结果于2021511日已经在《化学前沿》(Frontiers in Chemistry)杂志网站发表——Marta Dueñas-Díez, Juan Pérez-Mercader. Native Chemical Computation. A Generic Application of Oscillating Chemistry Illustrated With the Belousov-Zhabotinsky Reaction. A Review. Frontiers in Chemistry, 11 May 2021.DOI: 10.3389/fchem.2021.611120. fchem-09-611120.pdf ). https://doi.org/10.3389/fchem.2021.611120

一个分子包含了丰富的信息。它不仅包括各种组成原子的数量,还包括它们是如何排列的以及它们是如何相互连接的。在化学反应中,这些信息决定了结果并发生了变化。分子以可预测的方式碰撞、分裂、重组和重建。

圣达菲研究所的外聘教授、哈佛大学的物理学家和天体生物学家(physicist and astrobiologistJuan Pérez-Mercader说,还有另一种看待化学反应的方法,这是一种计算。计算设备是一种将信息作为输入,然后机械地转换该信息并产生具有功能目的的输出的设备。输入和输出几乎可以是任何东西:如数字、字母、对象、图像、符号或其他东西。

Juan Pérez-Mercader说,或者也可以是分子。当分子反应时,它们遵循着描述计算的相同步骤:输入、转换、输出。“这是一种控制特定事件何时发生的计算,”Juan Pérez-Mercader说,“但是在纳米尺度上,或更短的尺度上。”

分子可能很小,但它们作为计算工具的潜力是巨大的。他说:“这是一个非常强大的计算工具,需要加以利用。”他指出,一摩尔物质具有1023的基本化学处理器,能够进行计算。在过去的几年里,Juan Pérez-Mercader一直在开发一个他称之为“天然化学计算(native chemical computation)”的新领域。这是一个多方面的探索:他不仅想开发化学计算,还想找到最适合它的挑战。

他问道:“如果我们有如此强大的力量,我们能解决哪些问题?”他说,它们与那些可能用超级计算机更好地解决的问题不同。“那它们有什么用呢?

他有一些想法。他说,化学反应非常善于制造东西。所以在2017年,他的团队“编程”了一些化学反应("programmed" chemical reactions),用一堆分子组装一个容器。该实验证明,在某种意义上,这些分子可以识别信息,并以一种类似于计算的特定方式将其转换。

Juan Pérez-Mercader和他在该项目上的主要合作伙伴,哈佛大学和雷普索尔技术实验室(Repsol Technology Lab in Madrid)的化学工程师Marta Dueñas-Díez,最近在《化学前沿》杂志网站发表了一篇关于他们在化学计算方面进展的综述。在这篇文章中,他们描述了如何在实验室中使用化学反应来建立广泛的熟悉的计算系统,从简单的逻辑门到图灵机(Turing Machines)。Pérez-Mercader说,他们的发现表明,如果化学反应可以像其他类型的计算机一样被“编程”,它们可能会在许多领域得到应用,包括智能药物输送(intelligent drug delivery)、神经网络(neural networks),甚至人工细胞(artificial cells)。上述介绍,仅供参考。欲了解更多信息敬请注意浏览原文fchem-09-611120.pdf )或者相关报道

Abstract

Computing with molecules is at the center of complex natural phenomena, where the information contained in ordered sequences of molecules is used to implement functionalities of synthesized materials or to interpret the environment, as in Biology. This uses large macromolecules and the hindsight of billions of years of natural evolution.But, can one implement computation with small molecules? If so, at what levels in the hierarchy of computing complexity? We review here recent work in this area establishing that all physically realizable computing automata, from Finite Automata (FA) (such as logic gates) to the Linearly Bound Automaton (LBA, a Turing Machine with a finite tape) can be represented/assembled/built in the laboratory using oscillatory chemical reactions.We examine and discuss in depth the fundamental issues involved in this formof computation exclusively done by molecules. We illustrate their implementation with the example of a programmable finite tape Turing machine which using the Belousov-Zhabotinsky oscillatory chemistry is capable of recognizing words in a Context Sensitive Language and rejecting words outside the language.We offer a new interpretation of the recognition of a sequence of chemicals representing words in the machine’s language as an illustration of the “Maximum Entropy Production Principle” and concluding that word recognition by the Belousov-Zhabotinsky Turing machine is equivalent to extremal entropy production by the automaton.We end by offering some suggestions to apply the above to problems in computing, polymerization chemistry, and other fields of science.



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